date: 2022-09-19T09:20:35Z
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pdf:docinfo:title: Four Methods to Distinguish between Fractal Dimensions in Time Series through Recurrence Quantification Analysis
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Keywords: recurrence quantification analysis; fractals; monofractals; fractal time series
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subject: Fractal properties in time series of human behavior and physiology are quite ubiquitous, and several methods to capture such properties have been proposed in the past decades. Fractal properties are marked by similarities in statistical characteristics over time and space, and it has been suggested that such properties can be well-captured through recurrence quantification analysis. However, no methods to capture fractal fluctuations by means of recurrence-based methods have been developed yet. The present paper takes this suggestion as a point of departure to propose and test several approaches to quantifying fractal fluctuations in synthetic and empirical time-series data using recurrence-based analysis. We show that such measures can be extracted based on recurrence plots, and contrast the different approaches in terms of their accuracy and range of applicability.
dc:creator: Alon Tomashin, Giuseppe Leonardi and Sebastian Wallot
dcterms:created: 2022-09-19T09:10:33Z
Last-Modified: 2022-09-19T09:20:35Z
dcterms:modified: 2022-09-19T09:20:35Z
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title: Four Methods to Distinguish between Fractal Dimensions in Time Series through Recurrence Quantification Analysis
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pdf:docinfo:keywords: recurrence quantification analysis; fractals; monofractals; fractal time series
pdf:docinfo:modified: 2022-09-19T09:20:35Z
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dc:title: Four Methods to Distinguish between Fractal Dimensions in Time Series through Recurrence Quantification Analysis
modified: 2022-09-19T09:20:35Z
cp:subject: Fractal properties in time series of human behavior and physiology are quite ubiquitous, and several methods to capture such properties have been proposed in the past decades. Fractal properties are marked by similarities in statistical characteristics over time and space, and it has been suggested that such properties can be well-captured through recurrence quantification analysis. However, no methods to capture fractal fluctuations by means of recurrence-based methods have been developed yet. The present paper takes this suggestion as a point of departure to propose and test several approaches to quantifying fractal fluctuations in synthetic and empirical time-series data using recurrence-based analysis. We show that such measures can be extracted based on recurrence plots, and contrast the different approaches in terms of their accuracy and range of applicability.
pdf:docinfo:subject: Fractal properties in time series of human behavior and physiology are quite ubiquitous, and several methods to capture such properties have been proposed in the past decades. Fractal properties are marked by similarities in statistical characteristics over time and space, and it has been suggested that such properties can be well-captured through recurrence quantification analysis. However, no methods to capture fractal fluctuations by means of recurrence-based methods have been developed yet. The present paper takes this suggestion as a point of departure to propose and test several approaches to quantifying fractal fluctuations in synthetic and empirical time-series data using recurrence-based analysis. We show that such measures can be extracted based on recurrence plots, and contrast the different approaches in terms of their accuracy and range of applicability.
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pdf:docinfo:creator: Alon Tomashin, Giuseppe Leonardi and Sebastian Wallot
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creator: Alon Tomashin, Giuseppe Leonardi and Sebastian Wallot
meta:author: Alon Tomashin, Giuseppe Leonardi and Sebastian Wallot
dc:subject: recurrence quantification analysis; fractals; monofractals; fractal time series
meta:creation-date: 2022-09-19T09:10:33Z
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Author: Alon Tomashin, Giuseppe Leonardi and Sebastian Wallot
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